Structured relevance - a mechanism to reveal why data is related

ABSTRACT

A machine receives a group of members of a data set. The machine identifies key symbols from the members of the group or the data set. The machine then calculates, for each key symbol, a weighted magnitude for the key symbol in the group. The machine can then sort the key symbols according to their weighted magnitudes, and filter out common key symbols. The uncommon key symbols, as sorted according to their weighted magnitudes, can form a name for the group.

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No. ______,titled “STRUCTURED RELEVANCE—A MECHANISM TO REVEAL HOW DATA IS RELATED”,filed ______, which is commonly assigned with this application and ishereby incorporated by reference.

This application is related to U.S. patent application Ser. No.12/568,190, filed Sep. 28, 2009, titled “OPTIMAL SEQUENTIAL(DE)COMPRESSION OF DIGITAL DATA”, U.S. patent application Ser. No.12/575,767, filed Oct. 8, 2009, 2009, titled “FAST APPROXIMATION TOOPTIMAL COMPRESSION OF DIGITAL DATA”, U.S. patent application Ser. No.12/616,306, filed Nov. 11, 2009, titled “DIGITAL SPECTRUM OF FILE BASEDCONTENTS”, U.S. patent application Ser. No. 12/649,584, filed Dec. 30,2009, titled “OPTIMIZED PARTITIONS FOR GROUPING AND DIFFERENTIATINGFILES OF DATA”, U.S. patent application Ser. No. 12/649,688, filed Dec.30, 2009, titled “STOPPING FUNCTIONS FROM GROUPING AND DIFFERENTIATINGFILES BASED ON CONTENT”, U.S. patent application Ser. No. 12/637,807,filed Dec. 15, 2009, titled “GROUPING AND DIFFERENTIATING FILES BASED ONCONTENT”, and U.S. patent application Ser. No. 12/684,313, filed Jan. 8,2010, titled “GROUPING AND DIFFERENTIATING VOLUMES OF FILES”, all ofwhich claim the benefit of U.S. Provisional Patent Application Ser. No.61/271,079, filed Jul. 16, 2009 and U.S. Provisional Patent ApplicationNo. 61/236,571, filed Aug. 25, 2009, all of which are incorporated byreference.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

This invention pertains to organization of data, and more particularlyto why data is grouped together based on content.

BACKGROUND

The Internet is an excellent resource for searching for information.Over the years, many different search engines have been designed toprovide users with easy ways to search for data. Recent improvements insearch engines include predictive searching, where the search enginepredicts the user's search term based on what previous users havesearched for that start with the same terms.

But the organization of the data returned as a result of a search is notan area that is well developed. Search engines return the data organizedby what seems most likely to be desired by the user. While a reasonablestrategy when the search term is fairly specific, this strategy does notwork so well when the search terms are broad (either intentionally oraccidentally). For example, a search for the term “Paris” could resultin information relating to Paris, France (the city), Paris Hotel (thecasino in Las Vegas, NV), or Paris Hilton (the socialite). Because it islikely that different users searching for the term “Paris” have intendedall three targets, the results of a search for the term “Paris” willhave results for all three targets intermixed. Thus, a user interestedonly in Paris, France would need to manually skip over entries relatingto the casino and the socialite from the results. The situation becomeseven more complicated when the user might be interested in combinationsof the information: for example, information about the last time ParisHilton stayed at the Paris Hotel. And even knowing how the data isgrouped does not tell a person anything about why the data is groupedthe way it is.

A need remains for a way to address these and other problems associatedwith the prior art.

SUMMARY

A machine can receive group of members of a data set. The machine canidentify key symbols in the group of members. For each key symbol, themachine can calculate a weighted magnitude. The machine can sort the keysymbols according to their weighted magnitudes, filtering out common keysymbols. The uncommon key symbols, as sorted by their weighted magnitudecan form a name for the members of the group.

The foregoing and other features, objects, and advantages of theinvention will become more readily apparent from the following detaileddescription, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a machine to determine how members of a data set arerelated, according to an embodiment of the invention.

FIG. 2 shows how the machine of FIG. 1 can take descriptions ofrelationships among the members of a data set, construct a graphrepresenting those relationships, and use the graph to respond to aquery.

FIG. 3 shows an example of various groups in the graph of the members ofthe data set produced by the machine of FIG. 1.

FIG. 4 shows a flowchart of a procedure for constructing a graphrepresenting the relationships among members of a data set, according toan embodiment of the invention.

FIG. 5 shows a flowchart of a procedure to use the graph representingthe relationships among members of a data set to respond to a query,according to an embodiment of the invention.

FIG. 6 shows a machine that can join groups in the graph representingthe relationships among members of a data set, according to anembodiment of the invention.

FIG. 7 shows an example of two groups being joined by the machine ofFIG. 6.

FIG. 8 shows a flowchart of a procedure to join groups in the graphrepresenting the relationships among members of a data set, according toan embodiment of the invention.

FIG. 9 shows a machine that can determine why members of a data set arelated, according to an embodiment of the invention.

FIG. 10 shows details about the weighted magnitude calculator of FIG. 9.

FIG. 11 shows details about the sorter of FIG. 9.

FIG. 12 shows a flowchart of a procedure to describe why members of adata set a related, according to an embodiment of the invention.

FIG. 13 shows a flowchart of a procedure to calculate the weightedmagnitude for a key symbol, according to an embodiment of the invention.

DETAILED DESCRIPTION

Before describing how the invention solves the problem of understandinghow and why members of a data set are related, it is useful tounderstand a few example problems that the invention can solve:

-   A new employee at a company is given the task of inventorying a    large data server. The employee is told that the server was used by    a team that was recently disbanded, and that some key findings were    in one version of a file, but there is a concern that those key    findings had been deleted from a later version of the file. The    files were shared by various team members and the names are not    consistent and so it is impossible to tell from the file names if    the files are different versions of each other. The employee is told    to find all files that are related to each other and why. The    employee is told to not only try to find versions of these specific    known files but any other versions of any other files, since the CIO    expects if they had this one problem, there might be other instances    of the same problem in other files. In order to know that they    versions of files without reading each entire file in each relevancy    group, the employee needs to know how and why the data in each    relevancy group is related.-   Two companies are merged under somewhat hostile terms and one    research teams resigns en masse. They leave their data, but no    “baton passing” to understand what the data is and how it is    structured and what it means. They have terabytes of data that    remaining research teams need to comb through the data, not knowing    exactly what the research teams are looking for. Tools exist to find    related data, but then the research teams will want to know how and    why the data is related.-   A collaborative effort is underway by 5 different companies to find    out why quality assurance metrics are recently down on their related    manufacturing processes. They know that the end result is more    failures in a specific material that is being manufactured, but they    do not know why. They want to share their data with each other but    they do not know exactly what they are looking for. Once they find    related data, they want to know how and why it is related.

Now that some example problems that show the need for understanding howand why data is related have been described, the solution can beexplained. FIG. 1 shows a machine to determine how members of a data setare related, according to an embodiment of the invention. In FIG. 1,machine 105 is shown. Machine 105 can be any machine capable ofdetermining how and/or why data is grouped in a particular way. Examplesof forms machine 105 can take include servers, personal computers,mainframes, smart phones, and tablet computers, among otherpossibilities. Machine 105 can include computer 110, monitor 115,keyboard 120, and mouse 125. A person skilled in the art will recognizethat other components not shown in FIG. 1 can be included with machine105: for example, other input/output devices, such as a printer. Inaddition, FIG. 1 does not show some of the conventional internalcomponents of machine 105; for example, a central processing unit,memory, storage, etc. Finally, although FIG. 1 shows machine 105 as aconventional desktop computer, a person skilled in the art willrecognize that machine 105 can be any type of machine or computingdevice capable of providing the services attributed herein to machine105, including, for example, a laptop computer, PDA, or a cellulartelephone.

Machine 105 includes various components. Input port 130 is responsiblefor receiving information into machine 105. For example, input port 130can receive information about how various members of a data set arerelated, which can be used to determine how and why the members of thedata set are grouped in a particular manner. Input port 130 can alsoreceive a query, which can be answered using information about thevarious members of the data set.

Graph constructor 135 is responsible for constructing a graph thatrepresents the relationships among the members of the data set. Thisgraph makes it possible to more easily understand how the members of thedata set are grouped, and why.

Query results module 140 is responsible for processing a query andproducing results for that query. These results can factor in how themembers of the data set, over which the query applies, are related,making it easier to identify which members of the data set are mostappropriate in response to the query.

Group balance determiner 145 provides additional functionality, in thatthere may be groups in the data set that are not well balanced. Assumingan unbalanced group can be found using group balance determiner 145,heavy sub-tree splitter 150 can be used to adjust the groups, splittingoff heavy, or unbalanced, sub-trees to better balance the group.

Although FIG. 1 describes receiving information about how members of asingle data set are related, a person of ordinary skill in the art willrecognize that embodiments of the invention can be generalized tomultiple data sets. That is, machine 105 can process information abouthow members of multiple data sets are related. Essentially, the multipledata sets can be subject to a union operation, creating a single dataset including all the members of all the data sets, and machine 105 canthen information about how the members of this single data set arerelated.

FIG. 2 shows how the machine of FIG. 1 can take descriptions ofrelationships among the members of a data set, construct a graphrepresenting those relationships, and use the graph to respond to aquery. In FIG. 2, the machine (not shown in FIG. 2) can receiverelationship description 205, which describes relationships among themembers of a data set. For example, relationship description 205 canspecify, for each member of the data set, what other member of the dataset is closest to that member, and the distance between that member andits closest neighbor. This description of relationships is shown ininset 210, which is a subset of the relationships described in Table 1.

TABLE 1 member 0 is 12 units from member 37 member 1 is 24 units frommember 9 member 2 is 32 units from member 3 member 3 is 29 units frommember 27 member 4 is 14 units from member 95 member 5 is 27 units frommember 26 member 6 is 23 units from member 27 member 7 is 24 units frommember 27 member 8 is 11 units from member 73 member 9 is 14 units frommember 95 member 10 is 4 units from member 11 member 11 is 4 units frommember 10 member 12 is 13 units from member 11 member 13 is 5 units frommember 15 member 14 is 4 units from member 15 member 15 is 3 units frommember 16 member 16 is 3 units from member 15 member 17 is 4 units frommember 18 member 18 is 4 units from member 17 member 19 is 25 units frommember 27 member 20 is 27 units from member 27 member 21 is 25 unitsfrom member 27 member 22 is 25 units from member 80 member 23 is 28units from member 27 member 24 is 24 units from member 27 member 25 is27 units from member 88 member 26 is 12 units from member 29 member 27is 13 units from member 26 member 28 is 25 units from member 59 member29 is 12 units from member 26 member 30 is 22 units from member 27member 31 is 29 units from member 27 member 32 is 29 units from member27 member 33 is 19 units from member 27 member 34 is 26 units frommember 33 member 35 is 19 units from member 66 member 36 is 13 unitsfrom member 73 member 37 is 13 units from member 0 member 38 is 19 unitsfrom member 27 member 39 is 20 units from member 27 member 40 is 27units from member 27 member 41 is 34 units from member 27 member 42 is26 units from member 43 member 43 is 26 units from member 42 member 44is 27 units from member 42 member 45 is 24 units from member 46 member46 is 24 units from member 45 member 47 is 20 units from member 27member 48 is 15 units from member 51 member 49 is 28 units from member51 member 50 is 20 units from member 51 member 51 is 15 units frommember 48 member 52 is 12 units from member 54 member 53 is 30 unitsfrom member 68 member 54 is 12 units from member 52 member 55 is 25units from member 71 member 56 is 8 units from member 57 member 57 is 8units from member 58 member 58 is 8 units from member 57 member 59 is 9units from member 58 member 60 is 20 units from member 27 member 61 is21 units from member 63 member 62 is 24 units from member 59 member 63is 20 units from member 59 member 64 is 18 units from member 70 member65 is 16 units from member 71 member 66 is 14 units from member 71member 67 is 16 units from member 68 member 68 is 13 units from member71 member 69 is 16 units from member 68 member 70 is 17 units frommember 68 member 71 is 13 units from member 68 member 72 is 19 unitsfrom member 64 member 73 is 12 units from member 8 member 74 is 26 unitsfrom member 77 member 75 is 29 units from member 74 member 76 is 27units from member 74 member 77 is 26 units from member 74 member 78 is29 units from member 74 member 79 is 20 units from member 27 member 80is 19 units from member 27 member 81 is 21 units from member 27 member82 is 22 units from member 81 member 83 is 20 units from member 80member 84 is 21 units from member 27 member 85 is 22 units from member84 member 86 is 12 units from member 37 member 87 is 23 units frommember 11 member 88 is 18 units from member 26 member 89 is 21 unitsfrom member 59 member 90 is 24 units from member 59 member 91 is 28units from member 59 member 92 is 23 units from member 27 member 93 is21 units from member 27 member 94 is 20 units from member 27 member 95is 15 units from member 4

A person of ordinary skill in the art will recognize that the “closest”relationship, which can also be called a “nearest neighbor”relationship, depends on the data in the member itself, and thus is notnecessarily a symmetrical relationship, nor is the relationship based ona calculated distance between the members. That is, just because memberB is the nearest neighbor of member A does not automatically imply thatmember A is the nearest neighbor of member B. For example, it couldhappen that member B is the nearest neighbor of member A, but member Cis the nearest neighbor of member B. Further, it could happen that thedistance between members A and B is less than the distance betweenmembers B and C.

This non-symmetrical relationship can lead to some unusual results. Forexample, it is theoretically possible that member B is closest to memberA, member C is closest to member B, and member A is closes to member C,which would seem to be a contradictory result. But empirical testing hasnot encountered this situation: as discussed below with reference toFIG. 3, every group in empirical testing has had a core of two members.For purposes of this discussion, a “core” (or “core group”) is a set ofmembers of the data set such that there is a path from any member of thedata set in the core to each other member of the data set in the core,traversing via the nearest neighbor links. Put another way, the coreconsists of a set of members of the data set form a cycle.

There are several technologies available today that can provideinformation about the relationships among various members. For example,U.S. patent application Ser. No. 12/568,190, filed Sep. 28, 2009, titled“OPTIMAL SEQUENTIAL (DE)COMPRESSION OF DIGITAL DATA”, U.S. patentapplication Ser. No. 12/575,767, filed Oct. 8, 2009, 2009, titled “FASTAPPROXIMATION TO OPTIMAL COMPRESSION OF DIGITAL DATA”, U.S. patentapplication Ser. No. 12/616,306, filed Nov. 11, 2009, titled “DIGITALSPECTRUM OF FILE BASED CONTENTS”, U.S. patent application Ser. No.12/649,584, filed Dec. 30, 2009, titled “OPTIMIZED PARTITIONS FORGROUPING AND DIFFERENTIATING FILES OF DATA”, U.S. patent applicationSer. No. 12/649,688, filed Dec. 30, 2009, titled “STOPPING FUNCTIONSFROM GROUPING AND DIFFERENTIATING FILES BASED ON CONTENT”, U.S. patentapplication Ser. No. 12/637,807, filed Dec. 15, 2009, titled “GROUPINGAND DIFFERENTIATING FILES BASED ON CONTENT”, and U.S. patent applicationSer. No. 12/684,313, filed Jan. 8, 2010, titled “GROUPING ANDDIFFERENTIATING VOLUMES OF FILES”, all of which are incorporated byreference herein, are patent applications that describe ways ofdetermining relationships among members of a data set. But a personskilled in the art will recognize that there are other ways in which todetermine relationships between members of a data set. For example, U.S.patent application Ser. No. 09/109,804, filed Jul. 2, 1998, titled“METHOD AND APPARATUS FOR SEMANTIC CHARACTERIZATION OF GENERAL CONTENTSTREAMS AND REPOSITORIES”, now U.S. Pat. No. 6,108,619, issued Aug. 22,2000, U.S. patent application Ser. No. 09/512,963, filed Feb. 25, 2000,titled “CONSTRUCTION, MANIPULATION, AND COMPARISON OF AMULTI-DIMENSIONAL SEMANTIC SPACE”, now U.S. Pat. No. 7,152,031, issuedDec. 19, 2006, U.S. patent application Ser. No. 09/615,726, filed Jul.13, 2002, titled “METHOD AND MECHANISM FOR THE CREATION, MAINTENANCE,AND COMPARISON OF SEMANTIC ABSTRACTS”, now U.S. Pat. No. 7,197,451,issued Mar. 27, 2007, U.S. patent application Ser. No. 09/653,713, filedSep. 5, 2000, titled “INTENTIONAL STANCE CHARACTERIZATION OF A GENERALCONTENT STREAM OR REPOSITORY”, now U.S. Pat. No. 7,286,977, issued Oct.23, 2007, U.S. patent application Ser. No. 09/691,629, filed Oct. 18,2000, titled “METHOD AND MECHANISM FOR SUPERPOSITIONING STATE VECTORS INA SEMANTIC ABSTRACT”, now U.S. Pat. No. 7,389,225, issued Jun. 17, 2008,U.S. patent application Ser. No. 09/654,660, filed Sep. 5, 2000, titled“POLICY ENFORCEMENT USING THE SEMANTIC CHARACTERIZATION OF TRAFFIC”, nowU.S. Pat. No. 7,177,922, issued Feb. 13, 2007, U.S. patent applicationSer. No. 09/710,027, filed Nov. 7, 2000, titled “DIRECTED SEMANTICDOCUMENT PEDIGREE”, now abandoned, U.S. patent application Ser. No.11/554,476, filed Oct. 30, 2006, titled “INTENTIONAL-STANCECHARACTERIZATION OF A GENERAL CONTENT STREAM OR REPOSITORY”, now U.S.Pat. No. 7,562,011, issued Jul. 14, 2009, U.S. patent application Ser.No. 11/616,154, filed Dec. 26, 2006, titled “SYSTEM AND METHOD OFSEMANTIC CORRELATION OF RICH CONTENT”, now U.S. Pat. No. 7,672,952,issued Mar. 2, 2010, U.S. patent application Ser. No. 11/562,337, filedNov. 21, 2006, titled “CONSTRUCTION, MANIPULATION, AND COMPARISON OF AMULTI-DIMENSIONAL SEMANTIC SPACE”, now U.S. Pat. No. 7,475,008, issuedJan. 6, 2009, U.S. patent application Ser. No. 11/563,659, filed Nov.27, 2006, titled “METHOD AND MECHANISM FOR THE CREATION, MAINTENANCE,AND COMPARISON OF SEMANTIC ABSTRACTS”, now U.S. Pat. No. 7,653,530,issued Jan. 26, 2010, and U.S. patent application Ser. No. 11/929,678,filed Oct. 3, 2007, titled “CONSTRUCTION, MANIPULATION, ANDCOMPARISON-OF A MULTI-DIMENSIONAL SEMANTIC SPACE”, now allowed,(6647-0100), all of which are incorporated by reference herein, describeways to characterize semantic content, and could be the basis fordetermining relationships between members of a data set.

Returning to FIG. 2, graph constructor 135 takes the description ofrelationships 205, and uses them to produce graph 215, which graphicallyrepresents the relationships between the members of the data set. WhileFIG. 2 shows graph 215 as a simple graph with only three nodes in a treestructure, a person of ordinary skill in the art will recognize that thegraph can have any number of nodes (normally, one node for each memberof the data set), and does not have to be in a tree structure. In fact,as discussed below with reference to FIG. 3, the graph does not evenrequire that each pair of nodes be connected by some path. Instead, thenodes can be grouped together, so that nodes in one group are notconnected to nodes in another group.

Query results module 140 can take graph 215 and query 220 and producequery results 225, which is the results of the query as applied to thedata set. To that end, query results module 140 can include a bestmember identifier 230 that identifies the member of the data set thatbest responds to the query, and group identifier 235, which identifiesthe group to which the best member belongs. In this manner, othermembers of that group can be positioned as more responsive to the querythan members of the data set that are in another group.

FIG. 3 shows an example of various groups in the graph of the members ofthe data set produced by the machine of FIG. 1. In FIG. 3, severalgroups are shown from graph 215. As only a subset of the nodes are shown(for example, FIG. 3 does not show nodes 1-11, 16-33, 35, 37-43, 45-50,54-62, 70, 73-84, and 86-95 (assuming that the nodes are numberedsequentially to represent members of the data set described in Table 1),a person of ordinary skill in the art will recognize that there areseveral other groups not shown in graph 215. A person of ordinary skillin the art will also recognize that the distances shown in FIG. 3between nodes in groups is approximate, and the graph is not necessarilyrepresentative of the true distances between nodes.

Looking at graph 215, a few features are apparent. First, group 305 isthe smallest of the groups shown. It includes only two members (nodes 52and 54), each of which is the nearest neighbor of the other. In fact, ineach of groups 305, 310, 315, and 320, there are two nodes that are eachother's nearest neighbors. As mentioned above with reference to FIG. 2,there is nothing that requires each group in graph 215 to have twonodes, each of which is the nearest neighbor to the other. But empiricalresults have shown that this rule, if not always true, is true most ofthe time. For purposes of further discussion, this set of nodes within agroup, where each node is the nearest neighbor of the other, is called a“core”.

Groups 310, 315, and 320 become progressively more complex. Group 310includes a core with nodes 0 and 37, group 315 includes a core withnodes 15 and 16, and group 320 includes a core with nodes 68 and 72.Group 310 includes one additional node (node 86); group 315 includes twoadditional nodes (13 and 14); and group 320 includes 10 additional nodes(nodes 35, 45, 53, 64, 65, 66, 67, 69, 70, and 73).

For each group, a strength can be defined. The strength of a group iscombination of the group's order, weight, and distance. The order of agroup is the number of nodes in the group. The weight of a group is thedepth of the deepest sub-tree in the group: that is, the maximum numberof hops that need to be made from any node in the group to reach thecore. And the distance of a group is the average of the distances fromeach node to its nearest neighbor. A person of ordinary skill in the artwill understand that the term “average” means (mathematically) the meanof the distances. But a person of ordinary skill in the art will alsorecognize that an alternative method for calculating an “average” canused. For example, the “average” can be a weighted average, using aweight for each distance. Or, the “average” can be a geometric average.Or, the “average” can be (mathematically) the median or mode of thedistances. A person of ordinary skill in the art will recognize otherpossible “averages” that can be used.

For each of the order, weight, and distance, a smaller number indicatesa stronger group. The strength of the group can be represented as avector that includes these three values, where the order is moresignificant than the weight, and the weight is more significant than thedistance. Table 2 shows the strengths of the groups shown in FIG. 3:

TABLE 2 Group: Order: Weight: Distance: Strength: group 305 2 0 12  <2,0, 12> group 310 3 1 12.33  <3, 1, 12.33> group 315 4 1 3.75  <4, 1, 3.75> group 320 12 3 17.17 <12, 3, 17.17>

A few facts can be seen from Table 2. First, the strongest group isgroup 305, as it has the smallest order. In addition, as group 305 is acore group (that is, it includes no nodes that are not part of thecore), group 305 is considered stronger than non-core groups (that is,groups that include nodes that are not part of the core of the group).If there are multiple core groups (which can happen), then they wouldall have the same order (assuming the empirical results that cores haveonly two nodes holds true) and weight, which means that core groups canbe compared by their distances. Assuming the orders of the groups wereequal, then group 305 would be stronger than groups 310, 315, and 320(because it has a smaller weight). Groups 310 and 315 have equal weight;if they also had equal order, then the distance would determine whichgroup is stronger (group 315 would be considered stronger, because ithas a smaller average distance between nodes).

Group 320 is an interesting group, in that it has seven nodes (nodes 53,55, 65, 66, 67, 69, and 70) that have the core as their nearestneighbor. Each of these nodes is the root of a true sub-tree: there isno core group within any of the sub-trees. Only two of the sub-trees(rooted at nodes 66 and 70) have any nodes linked to them. The depth ofgroup 320 is determined from node 72, which is linked to the core vianodes 64 and 70 (a total of three hops to reach the core): no node isfurther from the core than node 72.

Groups 305, 310, 315, and 320 are all relatively small, and appear to bebalanced. A group is considered unbalanced if it includes a sub-treethat includes an excessive percentage of the total nodes in the group.Unbalanced groups tend to be weaker than balanced groups, and thus thereis reason to attempt to improve the graph by balancing the group.Empirical testing has determined that a usable threshold percentage ofnodes to unbalance a group is 50-60%: that is, if a sub-tree of thegroup includes more than 50-60% of the nodes in the group, then thegroup is considered unbalanced. Looking at group 320, for example, thelargest sub-tree is the sub-tree rooted at node 70, which includes atotal of three nodes. As the group as a whole has 12 nodes, thissub-tree only has 25% of the nodes, and the group is consideredbalanced. A person of ordinary skill in the art will recognize that thethreshold percentage can be set to any desired level, and that thesuggested threshold percentage of 50-60% is only a suggestion.

When a group is unbalanced, the sub-tree that has unbalanced the groupcan be split off from the group, and made into its own group. Obviously,this new group will not have a core, making it a non-core group.Non-core groups are considered weaker than core groups, without regardto the relative order, weight, or distance of the groups. A person ofordinary skill in the art will recognize that the remainder of theoriginal group might remain unbalanced: based on its new weight, theremight be a remaining sub-tree that again unbalances the group, in whichcase the group might again require a heavy sub-tree to be split off. Butthe overall graph is better balanced as a result.

FIG. 4 shows a flowchart of a procedure for constructing a graphrepresenting the relationships among members of a data set, according toan embodiment of the invention. In FIG. 4, at block 405, the systemidentifies the members of a data set. The data set itself can be definedindependently of embodiments of the invention; thus, the“identification” of the members of the data set can be independent ofembodiments of the invention. At block 410, the system identifiesrelationships among the members of the data set. As discussed above, anydesired technology can be used to determine the relationships among themembers of the data set; how the relationships among the members of thedata set are determined is independent of embodiments of the invention.

At block 415, the system identifies the nearest neighbor of each member.At block 420, the system determines the distance between a member andits nearest neighbor. At block 425, the system allocates each member toa group, based on its nearest neighbor. At block 430, the systemdetermines each group's strength (that is, each group's order, weight,and distance). At block 435, the system constructs a graph representingthe members of the data set and their relationships.

At block 440, the system determines if there is a group that isunbalanced. If a group is unbalanced, then at block 445, the systemsplits off a heavy sub-tree from the group. Control then returns toblock 440 to check for another unbalanced group. Otherwise, at block 450the system can use the graph to answer a query about the members of thedata set.

FIG. 5 shows a flowchart of a procedure to use the graph representingthe relationships among members of a data set to respond to a query,according to an embodiment of the invention. In FIG. 5, at block 505,the system identifies a member of the data set that best answers thequery. At block 510, the system identifies the group that includes themember of the data set that best answers the query. At block 515, thesystem returns the member of the data set that best answers the query.At block 520, the system sorts the other members of the group thatincludes the member that best answers the query, according to theirdistance from the member that best answers the query. At block 525, thesystem returns other members of the group that includes the member thatbest answers the query, based on their distance from the member thatbest answers the query.

FIG. 6 shows a machine that can join groups in the graph representingthe relationships among members of a data set, according to anembodiment of the invention. Machine 605 is similar to machine 105 ofFIG. 1: machine 605 can also be combined with machine 105, to providemultiple functionalities.

Machine 605 includes various components. Input port 610 is responsiblefor receiving groupings of members of data sets, as determined bydifferent sources. For example, two different users might independentlyprocess a data set as described in FIGS. 1-5, but using differentmethodologies, resulting in different groupings. Or, different usersmight process different data sets (using either the same of differentmethodologies), that now need to be combined. Input port 610 can receivethese graphs from the different users.

Join tester 615 is responsible for testing whether it is beneficial tojoin particular sets of groups from the two users. In general, the samenumber of groups are taken from each user's graph and combined, ormerged, to form a single new group. The combined group can then beanalyzed using the methodologies discussed above to determinerelationships among the members of the group. Join tester 615 can workwith one group at a time from each user, or with multiple groups fromeach user.

To test whether the combined group is stronger than the individualgroups from the users, join tester 615 can determine the strength (thatis, the order, weight, and distance) of the potential combined group. Ifthe strength of the potential combined group is stronger than the sum ofthe strengths of the individual groups, then combiner 620 combines, ormerges, the groups from the individual users into a merged group, whichis added to the graph; the original individual groups are removed fromthe respective user's graphs.

Output port 625 is responsible for outputting the result of merging anygroups, to produce a final graph, merging the data from the two originalgraphs.

FIG. 7 shows an example of two groups being joined by the machine ofFIG. 6. In FIG. 7, combiner 520 is shown receiving groups 705 and 710from different users, and producing combined group 715 instead. A personof ordinary skill in the art will recognize that groups 705, 710, and715, although represented using the same symbol as graph 215 of FIG. 2,are not intended to necessarily be the same as group 215 of FIG. 2.

FIG. 8 shows a flowchart of a procedure to join groups in the graphrepresenting the relationships among members of a data set, according toan embodiment of the invention. In FIG. 8, at block 805, the systemidentifies two pluralities of groups: that is, two graphs from dataanalysis performed by users. At block 810, the system identifies sets ofgroups from each graph, where each set has the same number of members.At block 815, the system merges the sets of groups. At block 820, thesystem receives a description of the relationships among the members ofthe merged group. At block 825, the system determines a strength (thatis, order, weight, and distance) for the merged group. At block 830, thesystem determines the sum of the strengths of the groups in the selectedsets.

At block 835, the system compares the strength of the merged group withthe summed strengths of the individual groups. If the merged group isstronger, then at block 840 the system replaces the individual groupswith the merged group. Either way, at block 845, the system determinesif there are more mergers to try. If so, then control returns to block810 to try more mergers. Otherwise, at block 850, the system outputs theresulting graph, including the merged groups.

FIG. 9 shows a machine that can determine why members of a data set arelated, according to an embodiment of the invention. Machine 905 issimilar to machine 105 of FIG. 1 and machine 605 of FIG. 6: machine 1405can also be combined with machines 105 and 605, to provide multiplefunctionalities.

Machine 905 includes various components. Input port 910 is responsiblefor receiving a group of members of a data set. Key symbol identifier915 is responsible for identifying key symbols in the members of thegroup. Key symbols are basically all symbols used in the members of thegroup, without regard to “common” symbols. For example, if the membersof the data set are documents written in English, the key symbolsinclude symbols such as “a”, “an”, “the”, “is”, and various other commonwords that are likely to be found in all documents.

Weighted magnitude calculator 920 is responsible for calculating theweighted magnitude for each key symbol identified by key symbolidentifier 915, within the group. And sorter 925 is responsible forsorting the key symbols based on their weighted magnitude. As describedbelow, this sorting can be used to provide a name for the group.

FIG. 10 shows details about the weighted magnitude calculator of FIG. 9.In FIG. 10, weighted magnitude calculator 920 includes frequencycalculator 1005, which determines a frequency for each key symbol ineach a given member of the group. Each key symbol's frequency isrelative to all symbols in that member of the group. When put insequence in a vector, the frequency of each key symbol in the member ofthe group forms a digital essence vector for the member of the group.

Maximum value calculator 1010 determines the maximum frequency of allkey symbols in all members of the group. Magnitude calculator 1015determines the magnitude of each key symbol. The magnitude of the keysymbol is the sum of the frequencies of the key symbol from each memberof the group, divided by the number of members in the group multipliedby the maximum value for any key symbol.

Variation calculator 1020 determines the variation of the key symbol.The variation of the key symbol measures the difference between thefrequency of the key symbol in a particular member of the group relativeto all key symbol frequencies in all members of the group. That is, thevariation is calculated by determining the absolute value of thedifference between the frequency of the key symbol in that document andthe frequency of every other value in every other digital essence vectorin the group. The absolute values of these differences can then besummed and normalized by dividing the sum by (n*(n−1))/2 times themaximum value for any key symbol, where n is the number of members inthe group. Variation calculator 1020 can also calculate the inversevariation, which is the difference between the maximum value for any keysymbol and the variation, using inverse variation calculator 1025.Calculating and using the inverse variation (as opposed to the standardvariation) means that the calculated weighted magnitudes for the keysymbols have their ordinary interpretation: that is, a larger weightedmagnitude implies a more important key symbol. But a person of ordinaryskill in the art will recognize that the weighted magnitude can becalculated using the standard variation, in which case smaller valuesare considered more significant.

Finally, product calculator 1030 determines the weighted magnitude bymultiplying the magnitude, as determined by magnitude calculator 1015,with the inverse variation, as determined by inverse variationcalculator 1025.

The above description assumes that all the members of the data set arepart of a single relevancy group. But as discussed above, there can bemultiple relevancy groups for a single data set. In that case, themagnitude, variation, and weighted magnitude (and inverse variation) canbe calculated based only on the members of a particular relevancy group(although the maximum value can be calculated across all members of thedata set).

FIG. 11 shows details about the sorter of FIG. 9. In FIG. 11, sorter 925includes common key symbol filter 1105. Common key symbol filter 1105filters out any common key symbols, which occur too frequently to beconsidered pertinent in interpreting the data set. The remaining keysymbols, the “uncommon” key symbols, when sorted using sorter 925, canform a name for the group. The name might not necessarily be readable orunderstandable to a human, but the name is at least electronicallydistinguishable from other groups.

In the above description, the weighted magnitudes of the key symbols aredetermined relative only to the members of the group. But a person ofordinary skill in the art will recognize that the key symbols can bedrawn from all members of the data set, not just those in the group. Inthat way, the weighted magnitude of the key symbols can be compared tothe weighted magnitude of the key symbols as computed for other groupsof members from the overall data set.

An example might prove useful to understanding how the weightedmagnitudes of the key symbols are calculated. Consider the (arbitrary)data set consisting of six members shown in Table 3:

TABLE 3 File name: File contents: F1 red red red blue blue blue F2 blueblue blue yellow yellow yellow yellow F3 yellow yellow blue red red redF4 one one one one two two three F5 one one two two two two three F6 oneone two two two three three three three three three

There are six key symbols in these six files: alphabetically, thesymbols are “blue”, “one”, “red”, “three” “two”, and “yellow”. (Forpurposes of understanding how embodiments of the invention operate tocalculate weighted magnitudes for the key symbols, the fact that thesekey symbols are all relatively common is ignored.) These files can begrouped into two relevancy groups. Files F1, F2, and F3 form onerelevancy group, and files F4, F5, and F6 form another relevancy group.

As can be seen, there are six key symbols in file F1. Three of these keysymbols are “red”, and three of these key symbols are “blue”. Thus,“red” comprises 50% of the key symbols in file F1, and “blue” comprises50% of the key symbols in file Fl; the other symbols comprise 0% of thefile. Thus, the digital essence vector for file F1 is <0.5, 0, 0.5, 0,0, 0>. In a similar manner, digital essence vectors can be constructedfor all six files; these digital essence vectors are shown in Table 4:

TABLE 4 File name: Digital essence vector: F1 <0.5, 0, 0.5, 0, 0, 0> F2<0.43, 0, 0, 0, 0, 0.57> F3 <0.17, 0, 0.5, 0, 0, 0.33> F4 <0, 0.57, 0,0.14, 0.29, 0> F5 <0, 0.29, 0, 0.14, 0.57, 0> F6 <0, 0.18, 0, 0.55,0.27, 0>

Note that the digital essence vector for a given file is independent ofthe relevancy group that includes the file.

A cursory examination of Table 4 also shows that the maximum value forany key symbol is 0.57 (shared by “yellow” in file F2, “one” in file F4,and “two” in file F5). In general, the maximum value is determinedrelative to the files in the relevancy group for which the weightedmagnitudes are being calculated. But in this example, the maximum valuefor any key symbol is the same in both relevancy groups. Thus, forpurposes of this example, the term “maximum value” could be used withoutqualifying to the relevancy group.

Using the digital essence vectors as a vector in N-dimensional space,each vector representing its corresponding file, the distance betweeneach pair of files can be calculated, using any desired distance metric.For example, Table 5 shows the distances between pairs of files measuredusing the Euclidean distance metric

${d\left( {x,y} \right)} = {{d\left( {y,x} \right)} = {\sqrt{\sum\limits_{i = 1}^{n}\left( {y_{i} - x_{i}} \right)^{2}}\text{:}}}$

TABLE 5 F1: F2: F3: F4: F5: F6: F1: 0.76 0.47 0.96 0.96 0.95 F2: 0.760.61 0.97 0.97 0.96 F3: 0.47 0.61 0.90 0.90 0.89 F4: 0.96 0.97 0.90 0.400.56 F5: 0.96 0.97 0.90 0.40 0.51 F6: 0.95 0.96 0.89 0.56 0.51

As can be seen in Table 5 the distance between vectors in N-dimensionalspace is symmetric: that is, the distance between files F1 and F2 doesnot depend on which file is the “starting point” for determining thedistance between the files. (Compare this with the “nearest neighbor”relationship described above, which is not a symmetric relationship.)While the Euclidean distance metric is a well-known metric for measuringdistance in N-dimensional space, a person of ordinary skill in the artwill also recognize that other distance metrics can be used: forexample, the taxicab geometry distance metric could be substituted forthe Euclidean distance metric.

For each file, its nearest neighbor can be determined. As can beintuitively understood, for a given file f, the nearest neighbor of filef is the file that minimizes the distance between file f and that otherfile. For example, the nearest neighbor to file F1 is file F3, becausethe distance between files F1 and F3 (0.47 units) is less than thedistance between file F1 and any other file. Table 6 shows the nearestneighbor for each of the six files:

TABLE 6 File: Nearest neighbor: F1 F3 F2 F3 F3 F1 F4 F5 F5 F4 F6 F5

Again, as noted previously, the nearest neighbor relationship is not asymmetric relationship: that is, the fact that a first file is nearestto a second file does not imply that the second file is nearest to thefirst file. For example, the nearest neighbor to file F2 is file F3, butthe nearest neighbor to file F3 is file F1.

Table 6 also shows how the relevancy groups can be formed. Files F1 andF3 form a core group (as each is the other's nearest neighbor), withfile F2 attached to this core group. Similarly, files F4 and F5 form acore group, with file F6 attached to this core group. Based on thisnearest neighbor relationship, there is nothing that links these tworelevancy groups.

Next, for each relevancy group, the magnitude of each key symbol can becalculated as the sum of all the values for that key symbol in thedigital essence vectors for each file in the relevancy group, scaled bythe maximum value for any key symbol in the relevancy group and thenumber of files in the relevancy group. Using “blue” as an example,“blue” has the values 0.5, 0.43, 0.17, 0, 0, and 0 in the six digitalessence vectors; the first three values come from members of the firstrelevancy group, and the last three values come from members of thesecond relevancy group. Thus, the magnitude for the key symbol “blue” inthe first relevancy group is (0.5+0.43+0.17)/(0.57*3)=1.1/1.71=0.64. Butthe magnitude for the key symbol “blue” in the second relevancy group is(0+0+0)/(0.57*3)=0/1.71=0. In a similar manner the magnitudes of theother key symbols can be calculated in each relevancy group. Table 7 andTable 8 show the magnitudes for the two relevancy groups.

The variation is calculated by summing the absolute value of thedifferences between the values for the key symbol in each pair ofmembers in the relevancy group, scaled by the number of calculateddifferences. Thus, the variation for the key symbol “blue” in the firstrelevancy group is (|0.5−0.43|+|0.5−0.17|+|0.43−0.17|)/3=0.22, whereasthe variation for the key symbol “blue” in the second relevancy group is(|0−0|+|0−0|+|0−0|)/3=0. In a similar manner the variations of the otherkey symbols can be calculated. Table 7 and Table 8 show the variationsfor the two relevancy groups.

The inverse variation is calculated by subtracting the variation fromthe maximum value for any key symbol in the relevancy group. Thus, theinverse variation for the key symbol “blue” in the first relevancy groupis 0.57−0.22=0.35, and the inverse variation for the key symbol “blue”in the second relevancy group is 0.57−0=0.57. In a similar manner theinverse variations of the other key symbols can be calculated. Table 7and Table 8 show the inverse variations for the two relevancy groups.Finally, the weighted magnitude is calculated by multiplying the inversevariation by the magnitude. Thus, the weighted magnitude for the keysymbol “blue” in the first relevancy group is 0.64*0.35=0.22, whereasthe weighted magnitude for the key symbol “blue” in the second relevancygroup is 0*0.57=0. In a similar manner the weighted magnitudes of theother key symbols can be calculated. Table 7 and Table 8 show theweighted magnitudes for the two relevancy groups.

TABLE 7 Key Inverse Weighted symbol Magnitude Variation variationmagnitude “blue” 0.64 0.22 0.35 0.22 “one” 0 0 0.57 0 “red” 0.58 0.330.24 0.14 “three” 0 0 0.57 0 “two” 0 0 0.57 0 “yellow” 0.53 0.38 0.190.10

TABLE 8 Key Inverse Weighted symbol Magnitude Variation variationmagnitude “blue” 0 0 0.57 0 “one” 0.61 0.26 0.31 0.19 “red” 0 0 0.57 0“three” 0.49 0.27 0.3 0.15 “two” 0.66 0.2 0.37 0.24 “yellow” 0 0 0.57 0

The key symbols can now be sorted by their weighted magnitude, whichshows their relative importance in each relevancy group. Thus, thesorted key symbols for the first relevancy group are “blue”, “red”, and“yellow”; the remaining key symbols are not relevant. Similarly, thesorted key symbols for the second relevancy group are “two”, “one”, and“three”, with the other key symbols not relevant.

These results make intuitive sense, as “blue is found in all the filesin the first relevancy group, whereas “red” and “yellow” are not.Similarly, “two” is found more frequently in the files in the secondrelevancy group than either “one” or “three”.

By taking the key symbols in their sorted order, names can beconstructed for the relevancy groups. Thus, the name for the firstrelevancy group can be “blue, red, yellow”, whereas the name for thesecond relevancy group can be “two, one, three”.

FIG. 12 shows a flowchart of a procedure to describe why members of adata set a related, according to an embodiment of the invention. In FIG.12, at block 1205, the system identifies a group of members of data set.At block 1210, the system determines the key symbols in the members ofthe group. As discussed above, the system can actually determine the keysymbols used in all members of the data set, and not just those membersof the data set that are part of the group. At block 1215, the systemdetermines a weighted magnitude for each key symbol.

At block 1220, the system filters out the common key symbols, leavingonly the “uncommon” key symbols. At block 1225, the system sorts the keysymbols according to their weighted magnitudes. At block 1230, thesorted uncommon key symbols are assigned as a name for the group.

FIG. 13 shows a flowchart of a procedure to calculate the weightedmagnitude for a key symbol, according to an embodiment of the invention.In FIG. 13, at block 1405, the system determines a frequency for eachsymbol in each member of the group, relative to all other key symbols inthe group. At block 1305, the system determines a magnitude for each keysymbol. At block 1310, the system determines a variation for each keysymbol.

At bock 1315, the system determines the maximum value for all keysymbols in the relevancy group. At block 1320, the system determines theinverse variation for all key symbols. At block 1325, the systemcalculates the weighted magnitude for all key symbols.

Appendix A below includes source code programmed in the Java®programming language that implements an embodiment of the invention asdescribed above in FIGS. 9-13. (Java is a registered trademark of OracleAmerica, Inc. and/or its affiliates.) A person of ordinary skill in theart will recognize that embodiments of the invention can be implementedin other programming languages, and that the source code shown inAppendix A is only one possible implementation using the Javaprogramming language.

Although the flowcharts shows the blocks as being performed in aparticular sequence, a person of ordinary skill in the art willrecognize that the blocks can be rearranged into other logicalsequences, which are considered parts of embodiments of the invention.In addition, various blocks can be omitted without comprising thefunctionality of embodiments of the invention. Finally, embodiments ofthe invention can be combined in any desired manner.

The following discussion is intended to provide a brief, generaldescription of a suitable machine in which certain aspects of theinvention can be implemented. Typically, the machine includes a systembus to which is attached processors, memory, e.g., random access memory(RAM), read-only memory (ROM), or other state preserving medium, storagedevices, a video interface, and input/output interface ports. Themachine can be controlled, at least in part, by input from conventionalinput devices, such as keyboards, mice, touch screens, etc., as well asby directives received from another machine, interaction with a virtualreality (VR) environment, biometric feedback, or other input signal. Asused herein, the term “machine” is intended to broadly encompass asingle machine, or a system of communicatively coupled machines ordevices operating together. Exemplary machines include computing devicessuch as personal computers, workstations, servers, portable computers,handheld devices, telephones, tablets, etc., as well as transportationdevices, such as private or public transportation, e.g., automobiles,trains, cabs, etc.

The machine can include embedded controllers, such as programmable ornon-programmable logic devices or arrays, Application SpecificIntegrated Circuits, embedded computers, smart cards, and the like. Themachine can utilize one or more connections to one or more remotemachines, such as through a network interface, modem, or othercommunicative coupling. Machines can be interconnected by way of aphysical and/or logical network, such as an intranet, the Internet,local area networks, wide area networks, etc. One skilled in the artwill appreciate that network communication can utilize various wiredand/or wireless short range or long range carriers and protocols,including radio frequency (RF), satellite, microwave, Institute ofElectrical and Electronics Engineers (IEEE) 545.11, Bluetooth, optical,infrared, cable, laser, etc.

The invention can be described by reference to or in conjunction withassociated data including functions, procedures, data structures,application programs, instructions, etc. which, when accessed by amachine, result in the machine performing tasks or defining abstractdata types or low-level hardware contexts. Associated data can be storedin, for example, the volatile and/or non-volatile memory, e.g., RAM,ROM, etc., or in other storage devices and their associated storagemedia, including hard-drives, floppy-disks, optical storage, tapes,flash memory, memory sticks, digital video disks, biological storage,and other tangible, physical storage media. Associated data can also bedelivered over transmission environments, including the physical and/orlogical network, in the form of packets, serial data, parallel data,propagated signals, etc., and can be used in a compressed or encryptedformat. Associated data can be used in a distributed environment, andstored locally and/or remotely for machine access.

Having described and illustrated the principles of the invention withreference to illustrated embodiments, it will be recognized that theillustrated embodiments can be modified in arrangement and detailwithout departing from such principles, and can be combined in anydesired manner. And although the foregoing discussion has focused onparticular embodiments, other configurations are contemplated. Inparticular, even though expressions such as “according to an embodimentof the invention” or the like are used herein, these phrases are meantto generally reference embodiment possibilities, and are not intended tolimit the invention to particular embodiment configurations. As usedherein, these terms can reference the same or different embodiments thatare combinable into other embodiments.

Consequently, in view of the wide variety of permutations to theembodiments described herein, this detailed description and accompanyingmaterial is intended to be illustrative only, and should not be taken aslimiting the scope of the invention. What is claimed as the invention,therefore, is all such modifications as can come within the scope andspirit of the following claims and equivalents thereto.

1. An apparatus, comprising: a computer; an input port to receive agroup containing at least one member of a data set containing aplurality of members; a key symbol identifier to identify at least onekey symbol in at least one member of said group; a weighted magnitudecalculator to calculate a weighted magnitude for each key symbol; and asorter to sort said at least one key symbol based on said weightedmagnitude for each key symbol, where the weighted magnitudes of the keysymbols represent why said members of said group are related.
 2. Anapparatus according to claim 1, wherein the key symbol identifier isoperative to identify all key symbols in all members of said data set.3. An apparatus according to claim 1, wherein the sorter includes acommon key symbol filter to filter out at least one common key symbolfrom the at least one key symbol, producing at least one uncommon keysymbol.
 4. An apparatus according to claim 3, wherein said at least oneuncommon key symbol forms a name for said group.
 5. An apparatusaccording to claim 1, wherein the weighted magnitude calculatorincludes: a frequency calculator to calculate a frequency of each keysymbol relative to all other key symbols in a member of said group; amagnitude calculator to calculate a magnitude of each key symbol fromall members of said group; a variation calculator to calculate avariation for each key symbol relative to said frequency of each otherkey symbol in each other member of said group; and a product calculatorto calculate said weighted magnitude as a product of said magnitude andsaid variation.
 6. An apparatus according to claim 6, wherein: theweighted magnitude calculator includes a maximum value calculator tocalculate a maximum value across all key symbols in all members of saidgroup; and the variation calculator includes an inverse variationcalculator to subtract said variation of each key symbol from saidmaximum value for all key symbols in all members of said group.
 7. Amethod, comprising: using a processor, identifying a group containing atleast one member of a data set containing a plurality of members;identifying a plurality of key symbols in a group containing at leastone member of a data set containing a plurality of members; determininga weighted magnitude for each key symbol; and sorting the key symbolsaccording to their weighted magnitudes, where the weighted magnitudes ofthe key symbols represent why the members of the group are related.
 8. Amethod according to claim 7, wherein identifying a plurality of keysymbols includes identifying all key symbols in all members of the dataset.
 9. A method according to claim 7, wherein sorting the key symbolsincludes filtering common key symbols out from the plurality of keysymbols, producing a plurality of uncommon key symbols.
 10. A methodaccording to claim 9, wherein sorting the key symbols includes furtherassigning the uncommon key symbols as a name of the group.
 11. A methodaccording to claim 7, wherein determining a weighted magnitude for eachkey symbol includes: for each member of the group, determining afrequency of the key symbol relative to all other key symbols in thatmember of the group; determining a magnitude of the key symbol from allmembers of the group; determining a variation of the key symbol relativeto the frequency of each other key symbol in each other member of thegroup; and determining the weighted magnitude as a product of themagnitude and the variation.
 12. A method according to claim 11, whereindetermining a variation of the key symbol includes: determining amaximum value for all key symbols in all members of the group; andsubtracting the variation of the key symbol from the maximum value forall key symbols in all members of the group.
 13. An article comprising anon-transitory storage medium, said non-transitory storage medium havingstored thereon instructions, that, when executed by a machine, resultin: identifying a group containing at least one member of a data setcontaining a plurality of members; identifying a plurality of keysymbols in a group containing at least one member of a data setcontaining a plurality of members; determining a weighted magnitude foreach key symbol; and sorting the key symbols according to their weightedmagnitudes, where the weighted magnitudes of the key symbols representwhy the members of the group are related.
 14. An apparatus according toclaim 13, wherein identifying a plurality of key symbols includesidentifying all key symbols in all members of the data set.
 15. Anarticle according to claim 13, wherein sorting the key symbols includesfiltering common key symbols out from the plurality of key symbols,producing a plurality of uncommon key symbols.
 16. An article accordingto claim 15, wherein sorting the key symbols includes further assigningthe uncommon key symbols as a name of the group.
 17. An articleaccording to claim 13, wherein determining a weighted magnitude for eachkey symbol includes: for each member of the group, determining afrequency of the key symbol relative to all other key symbols in thatmember of the group; determining a magnitude of the key symbol from allmembers of the group; determining a variation of the key symbol relativeto the frequency of each other key symbol in each other member of thegroup; and determining the weighted magnitude as a product of themagnitude and the variation.
 18. An article according to claim 17,wherein determining a variation of the key symbol includes: determininga maximum value for all key symbols in all members of the group; andsubtracting the variation of the key symbol from the maximum value forall key symbols in all members of the group.